Preclinical Efficacy and Safety Assessment of an Antibody–Drug Conjugate Targeting the c-RET Proto-Oncogene for Breast Carcinoma
Bibliographic record
Abstract
PURPOSE: The RET proto-oncogene has been implicated in breast cancer, and the studies herein describe the preclinical and safety assessment of an anti-RET antibody-drug conjugate (ADC) being developed for the treatment of breast cancer. EXPERIMENTAL DESIGN: RET protein expression was analyzed in breast tumor samples using tissue microarrays. The fully human anti-RET antibody (Y078) was conjugated to the DM1 and DM4 derivatives of the potent cytotoxic agent maytansine using thioether and disulfide linkers, respectively. The resulting compounds, designated Y078-DM1 and Y078-DM4, were evaluated for antitumor activity using human breast cancer cell lines and established tumor xenograft models. A single-dose, 28-day, safety study of Y078-DM1 was performed in cynomolgus monkeys. RESULTS: By immunohistochemistry, RET expression was detected in 57% of tumors (1,596 of 2,800 tumor sections) and was most common in HER2-positive and basal breast cancer subtypes. Potent in vitro cytotoxicity was achieved in human breast cancer cell lines that have expression levels comparable with those observed in breast cancer tissue samples. Dose-response studies in xenograft models demonstrated antitumor activity with both weekly and every-3-weeks dosing regimens. In cynomolgus monkeys, a single injection of Y078-DM1 demonstrated dose-dependent, reversible drug-mediated alterations in blood chemistry with evidence of on-target neuropathy. CONCLUSIONS: RET is broadly expressed in breast cancer specimens and thus represents a potential therapeutic target; Y078-DM1 and Y078-DM4 demonstrated antitumor activity in preclinical models. Optimization of the dosing schedule or an alternate cytotoxic agent with a different mechanism of action may reduce the potential risk of neuropathy. Clin Cancer Res; 21(24); 5552-62. ©2015 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".